Conditioned on \(T_{i}(t)\) , the worker is assumed to make linear progress
through the dataset, i.e., it takes \(kT_{i}(t)/b\)  to calculate \(k\) 
gradients (note that \(k\ge b\)  is allowed). In our simulations, we
choose \(\lambda =2/3\)  and \(\xi =1\) . Per our model, in both AMB and
AMB-DG, node \(i\)  is given a fixed time \(T_{p}\)  to compute \(b_{i}(t)\) 
gradients in epoch \(t\) . Per our assumptions above, \(b_{i}(t)=bT_{p}/T_{i}(t)\) ,
for some fixed \(b\le \mathbb {E}[b_{i}(t)]\) . In our simulations, we
assume a network of \(n=10\)  workers and that \(T_{p}=2.5\) . We choose
\(b=60\)  to ensure that the expected minibatch per epoch is \(\mathbb {E}[b(t)]\ge nb=600\) .
This lower bound is according to Theorem 7 of [1]}. Moreover,
in our examples, we set \(T_{c}=10\) .
